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Machine Learning-Based Phenomapping in Patients with Heart Failure and Secondary Prevention Implantable
Yu Deng1, Sijing Cheng1, Hao Huang1
1Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China.
Insights
Machine learning identified distinct patient groups for heart failure (HF) patients receiving secondary prophylactic implantable cardioverter-defibrillator (ICD) therapy. This phenomapping approach aids personalized treatment strategies for HF.
Area of Science:
- Cardiology
- Machine Learning
- Medical Informatics
Background:
- Risk stratification is crucial but underexplored in heart failure (HF) patients undergoing secondary prophylactic implantable cardioverter-defibrillator (ICD) implantation.
- Machine learning-based phenomapping offers a novel approach to stratify these heterogeneous patient populations.
Purpose of the Study:
- To evaluate the efficacy of machine learning-based phenomapping using routine clinical data for identifying distinct patient subgroups in HF.
- To assess if these identified subgroups differ in characteristics and prognosis, particularly regarding appropriate ICD shocks and mortality.
Main Methods:
- Utilized hierarchical k-means clustering on factor analysis of mixed data (FAMD) for phenomapping.
- Included 389 patients with chronic HF and ICDs, analyzing 44 baseline variables.
- Validated phenogroups by comparing baseline features and outcomes (appropriate shock, all-cause death).
Main Results:
- Identified three distinct phenogroups with significant differences in demographics, comorbidities, cardiac structure/function, and HF progression.
- Phenogroup 3 showed the worst HF progression, with significantly higher risks for appropriate shocks and all-cause death compared to Phenogroup 1.
- Phenogroup 2 exhibited an increased risk of appropriate shocks but not mortality compared to Phenogroup 1.
Conclusions:
- Machine learning-based phenomapping successfully identifies distinct phenotype subgroups in HF patients receiving secondary prophylactic ICD therapy.
- This novel strategy holds potential for advancing personalized medicine approaches in managing these patients.
Background:
Previous studies have failed to implement risk stratification in patients with heart failure (HF) who are eligible for secondary implantable cardioverter-defibrillator (ICD) implantation. We aimed to evaluate whether machine learning-based phenomapping using routinely available clinical data can identify subgroups that differ in characteristics and prognoses.
Methods:
A total of 389 patients with chronic HF implanted with an ICD were included, and forty-four baseline variables were collected. Phenomapping was performed using hierarchical k-means clustering based on factor analysis of mixed data (FAMD). The utility of phenomapping was validated by comparing the baseline features and outcomes of the first appropriate shock and all-cause death among the phenogroups.
Results:
During a median follow-up of 2.7 years for device interrogation and 5.1 years for survival status, 142 (36.5%) first appropriate shocks and 113 (29.0%) all-cause deaths occurred. The first 12 principal components extracted using the FAMD, explaining 60.5% of the total variability, were left for phenomapping. Three mutually exclusive phenogroups were identified. Phenogroup 1 comprised the oldest patients with ischemic cardiomyopathy; had the highest proportion of diabetes mellitus, hypertension, and hyperlipidemia; and had the most favorable cardiac structure and function among the phenogroups. Phenogroup 2 included the youngest patients, mostly those with non-ischemic cardiomyopathy, who had intermediate heart dimensions and function, and the fewest comorbidities. Phenogroup 3 had the worst HF progression. Kaplan-Meier curves revealed significant differences in the first appropriate shock (p = 0.002) and all-cause death (p 0.001) across the phenogroups. After adjusting for medications in Cox regression, phenogroups 2 and 3 displayed a graded increase in appropriate shock risk (hazard ratio [HR] 1.54, 95% confidence interval [CI] 1.03-2.28, p = 0.033; HR 2.21, 95% CI 1.42-3.43, p 0.001, respectively; p for trend 0.001) compared to phenogroup 1. Regarding mortality risk, phenogroup 3 was associated with an increased risk (HR 2.25, 95% CI 1.45-3.49, p 0.001). In contrast, phenogroup 2 had a risk (p = 0.124) comparable with phenogroup 1.
Conclusions:
Machine-learning-based phenomapping can identify distinct phenotype subgroups in patients with clinically heterogeneous HF with secondary prophylactic ICD therapy. This novel strategy may aid personalized medicine for these patients.
